CNN-based methods predict a limited number of images in a single pass. They focus on spatial appearances rather than temporal coherence. They are also used for video prediction, although they only create representations for fixed size inputs. Qui et al. qiu2017short use a multi-task CNN that explicitly includes features of the various radar stations to improve their CNN's quality. Ayzel et al. ayzel2019all used the all convolutional net [1]} for precipitation nowcasting. Recently, U-net [2]} is used by precipitation nowcasting methods[3]}, [4]}, [5]}, [6]}. Samsi et al. samsi2019distributed proposed a data-parallel model to speed up the training time. Their U-net model takes as input a temporal sequence of 7 images and outputs 6 images in the future. They used MSE loss for training. Agrawal et al. agrawal2019machine proposed a model that performs three binary classifications that indicate whether the rate exceeds thresholds that roughly correspond to trace rain, light rain and moderate rain. Ayzel et al. proposed RainNet [5]} that predicts continuous radar echo maps at a lead time of five minutes based on the past 4 consecutive maps. This recursive approach was repeated up to a maximum lead time of 60 minutes. Unfortunately, the spatial smoothing was an undesirable property of RainNet prediction and became increasingly apparent at longer lead times. Trebing et al. trebing2020smaat predict single precipitation map using the past 12 consecutive maps. They used depthwise-separable convolutions in order to reduce the number of parameters. Some methods [8]}, [9]} used a CNN models to predict one future precipitation value. Xiang et al. xiang2020precipitation proposed a mechanism to deal with missing radar data using the features extracted from the historical radar map sequence. Socaci et al. [10]} introduced methodology for radar data prediction using the Xception [11]} deep learning model.
